Artificial Intelligence Research in Smart Cities: A Bibliometric Analysis

Öz

This study presents a comprehensive bibliometric analysis examining the applications of artificial intelligence (AI) in smart cities in terms of global research trends, thematic evolution, and future directions. Based on the analysis of 810 publications from the Web of Science database covering the period 2004-2025, the study maps the intellectual structure of the field through co-citation, co-authorship, and keyword co-occurrence networks using VOSviewer software, and conducts thematic content analysis of the most influential publications. The findings reveal the application areas of AI in smart cities under six main headings: smart cities, AI and machine learning, internet of things, management, data security and blockchain, environmental sustainability, and digital twins for urban planning. Publication trends show an exponential increase in research outputs after 2019, with China, India, and the USA standing out as the countries with the most contributions. "Smart city", "AI", "machine learning", and "internet of things" are among the most frequently encountered keywords. The study identifies critical challenges such as the need for standard data protocols, ethical AI governance, and interdisciplinary collaboration for scalable and equitable urban solutions. The results highlight the transformative potential of AI in smart city development, while highlighting the importance of balanced approaches that integrate technological innovation with societal and environmental elements. This research serves as an essential reference for policymakers, local governments, urban planners, and researchers working at the intersection of AI and smart urban systems.

Anahtar Kelimeler

• Smart cities
• artificial intelligence
• Machine learning
• Internet of things
• Bibliometric analysis

Akıllı Şehirlerde Yapay Zeka Araştırmaları: Bibliyometrik Bir İnceleme

Öz

Bu çalışma, yapay zekanın (YZ) akıllı şehirlerdeki uygulamalarını küresel araştırma eğilimleri, tematik evrim ve gelecek yönelimler açısından inceleyen kapsamlı bir bibliyometrik analiz sunmaktadır. Web of Science (WoS) veri tabanından 2004-2025 dönemini kapsayan 810 yayının analizine dayanan çalışmada, VOSviewer yazılımı kullanılarak ortak atıf, ortak yazarlık ve anahtar kelime eş-oluşum ağlarıyla alanın entelektüel yapısı haritalanmış ve en etkili yayınların tematik içerik analizi yapılmıştır. Bulgular, YZ'nin akıllı şehirlerdeki uygulama alanlarını altı ana başlıkta ortaya koymaktadır: akıllı şehirler, YZ ve makine öğrenmesi, nesnelerin interneti, yönetimi, veri güvenliği ve blockchain, çevresel sürdürülebilirlik ile kentsel planlama için dijital ikizler. Yayın eğilimleri, 2019 ile 2024 yılları arasında araştırma çıktılarında hızlı bir artış olduğunu gösterirken, 2025 verileri yalnızca kısmi yıl yayın faaliyetini yansıtmaktadır ve bu nedenle ihtiyatlı bir şekilde yorumlanmalıdır. "Akıllı şehir", "yapay zeka", "makine öğrenmesi" ve "nesnelerin interneti" en sık karşılaşılan anahtar kelimeler arasındadır. Çalışma, ölçeklenebilir ve adil kentsel çözümler için standart veri protokolleri, etik YZ yönetişimi ve disiplinlerarası iş birliği ihtiyacı gibi kritik zorlukları tanımlamaktadır. Sonuçlar, YZ'nin akıllı şehir gelişimindeki dönüştürücü potansiyelini vurgularken, teknolojik yeniliği toplumsal ve çevresel unsurlarla bütünleştiren dengeli yaklaşımların önemine dikkat çekmektedir. Bu araştırma, YZ ve akıllı kentsel sistemler kesişiminde çalışan politikacılar, yerel yönetimler, şehir plancıları ve araştırmacılar için temel bir referans niteliği taşımaktadır.

Anahtar Kelimeler

• Akıllı şehirler
• Yapay zekâ
• Nesnelerin interneti
• makine öğrenimi
• Bibliyometrik analiz

Kaynakça

1. Ahad, M. A., Paiva, S., Tripathi, G., & Feroz, N. (2020). Enabling technologies and sustainable smart cities. Sustainable cities and society, 61, 102301. https://doi.org/10.1016/j.scs.2020.102301
2. Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, data management, and ethical challenges. Computer Science Review, 43, 100452. https://doi.org/10.1016/j.cosrev.2021.100452
3. Alaeddini, M., Hajizadeh, M., & Reaidy, P. (2023). A bibliometric analysis of research on the convergence of artificial intelligence and blockchain in smart cities. Smart Cities, 6(2), 764-795. https://doi.org/10.3390/smartcities6020037
4. Alahakoon, D., Nawaratne, R., Xu, Y., De Silva, D., Sivarajah, U., & Gupta, B. (2023). Self-building artificial intelligence and machine learning to empower big data analytics in smart cities. Information Systems Frontiers, 25(1), 221-240. https://doi.org/10.1007/s10796-020-10056-x
5. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
6. Allam, Z., & Jones, D. S. (2020, February). On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. In Healthcare (Vol. 8, No. 1, p. 46). MDPI. https://doi.org/10.3390/healthcare8010046
7. Allam, Z., Sharifi, A., Bibri, S. E., Jones, D. S., & Krogstie, J. (2022). The metaverse as a virtual form of smart cities: Opportunities and challenges for environmental, economic, and social sustainability in urban futures. Smart Cities, 5(3), 771-801. https://doi.org/10.3390/smartcities5030040
8. Alsabt, R., Adenle, Y. A., & Alshuwaikhat, H. M. (2024). Exploring the roles, future impacts, and strategic integration of artificial intelligence in the optimization of Smart City—from systematic literature review to conceptual model. Sustainability, 16(8), 3389. https://doi.org/10.3390/su16083389

9. Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on collaborative smart drones and Internet of Things for improving smartness of smart cities. IEEE Access, 7, 128125–128152. https://doi.org/10.1109/ACCESS.2019.2934998
10. Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., Tsarchopoulos, P., & Panori, A. (2018). Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 9(2), 146-169. https://doi.org/10.1108/JSTPM-05-2017-0016
11. Avcı, E. (2024). Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi. Kent Akademisi, 17(5), 1654-1675. https://doi.org/10.35674/kent.1490925
12. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
13. Bibri, SE and Krogstie, J. 2021. A Novel Model for Data-Driven Smart Sustainable Cities of the Future: A Strategic Roadmap to Transformational Change in the Era of Big Data. Future Cities and Environment, 7(1): 3, 1–25. https://doi.org/10.5334/fce.116
14. Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research. Scientometrics, 22(1), 155–205. https://doi.org/10.1007/BF02019280
15. Caragliu, A., & Del Bo, C. F. (2023). Smart cities and the urban digital divide. Npj urban sustainability, 3(1), 43. https://doi.org/10.1038/s42949-023-00117-w
16. Deng, T., Zhang, K., & Shen, Z. J. M. (2021). A systematic review of a digital twin city: A new pattern of urban governance toward smart cities. Journal of management science and engineering, 6(2), 125-134. https://doi.org/10.1016/j.jmse.2021.03.003
17. Eren, F. & Çay, K. (2025). Elements of a Digital Urbanisation Strategy for Türkiye Evidence from Psychometric Testing. Social Sciences, 14(89). https://doi.org/10.3390/socsci14020089
18. Fan, W. Q., Ismail, A. S., Mohammed, F., & Mukred, M. (2025). AI-Driven Smart City Security and Surveillance System: A Bibliometric Analysis. In Current and Future Trends on AI Applications: Volume 1 (pp. 305-328). Cham: Springer Nature Switzerland.
19. Ghadami, N., Gheibi, M., Kian, Z., Faramarz, M. G., Naghedi, R., Eftekhari, M., Fathollahi-Fard A.M., Dulebenets M. A. & Tian, G. (2021). Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods. Sustainable Cities and Society, 74, 103149. https://doi.org/10.1016/j.scs.2021.103149
20. Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357-367. https://doi.org/10.1023/A:1022378804087
21. Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181-217. https://doi.org/10.1002/jrsm.1378
22. Haque, A. B., Nur, A., & Chowdhury, R. N. (2023). Artificial intelligence in smart city-systematic literature review of current knowledge and future research avenues. Enabling technologies for effective planning and management in sustainable smart cities, 53-77.
23. Javed, A. R., Shahzad, F., ur Rehman, S., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022). Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. Cities, 129, 103794. https://doi.org/10.1016/j.cities.2022.103794
24. Karger, E., Rothweiler, A., Brée, T., & Ahlemann, F. (2025). Building the Smart City of Tomorrow: A Bibliometric Analysis of Artificial Intelligence in Urbanization. Urban Science, 9(4), 132. https://doi.org/10.3390/urbansci9040132
25. Kitchin, R. (2019). The timescape of smart cities. Annals of the American Association of Geographers, 109(3), 775-790. https://doi.org/10.1080/24694452.2018.1497475
26. Kumar, M., & Singh, A. (2022). Probabilistic data structures in smart city: Survey, applications, challenges, and research directions. Journal of Ambient Intelligence and Smart Environments, 14(4), 229-284. https://doi.org/10.3233/AIS-220101
27. Kumar, N. M., Goel, S., & Mallick, P. K. (2018). Smart cities in India: Features, policies, current status, and challenges. 2018 Technologies for Smart-City Energy Security and Power (ICSESP), 1-4. https://doi.org /10.1109/ICSESP.2018.8376669
28. Kumari, N., Kondala, M., & Mohammed, A. A. (2025). Analysing the Research Landscape of Smart Cities and Artificial Intelligence: A Study Utilizing R Studio and VOSviewer. In Artificial Intelligence and IoT for Cyber Security Solutions in Smart Cities (pp. 16-31). Chapman and Hall/CRC.
29. Le, L. T., Nguyen, H., Zhou, J., Dou, J., & Moayedi, H. (2019). Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Applied Sciences, 9(13), 2714. https://doi.org/10.3390/app9132714
30. Li, D., Yu, W., & Shao, Z. (2021). Smart city based on digital twins. Computational Urban Science, 1(1), 4. https://doi.org/10.1007/s43762-021-00005-y
31. Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: A multidisciplinary comparison of coverage via citations. Scientometrics, 126(1), 871-906. https://doi.org/10.1007/s11192-020-03690-4
32. Mongo, M., & Daidj, N. (2025). Digital twin: a driver of sustainable smart cities? Evidence from a bibliometric analysis. Environmental Economics and Policy Studies, 1-29. https://doi.org/10.1007/s10018-025-00437-4
33. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today's academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
34. Rahman, M. A., Rashid, M. M., Hossain, M. S., Hassanain, E., Alhamid, M. F., & Guizani, M. (2019). Blockchain and IoT-based cognitive edge framework for sharing economy services in a smart city. IEEE Access, 7, 18611–18621. https://doi.org/10.1109/ACCESS.2019.2896065
35. Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101, 63-80. https://doi.org/10.1016/j.comnet.2015.12.023
36. Regalado-Pezua, O., & Christofle, S. (2025). Future research opportunities on sustainable smart cities: bibliometric analysis and network visualization. Discover Sustainability, 6(1), 187. https://doi.org/10.1007/s43621-025-00983-7
37. Serban, A. C., & Lytras, M. D. (2020). Artificial intelligence for smart renewable energy sector in Europe—Smart energy infrastructures for next generation smart cities. IEEE Access, 8, 77364–77377. https://doi.org/10.1109/ACCESS.2020.2990123
38. Sharifi, A., Amirzadeh, M., & Khavarian-Garmsir, A. R. (2025). The metaverse as a future form of smart cities: A systematic literature review of co-benefits and trade-offs for sustainable development goals. Cities, 161, 105879. https://doi.org/10.1016/j.cities.2025.105879
39. Sharma, H., Haque, A., & Blaabjerg, F. (2021). Machine learning in wireless sensor networks for smart cities: A survey. Electronics, 10(9), 1012. https://doi.org/10.3390/electronics10091012
40. Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I. H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable cities and society, 63, 102364. https://doi.org/10.1016/j.scs.2020.102364
41. Srihith, I. V. D., Kumar, I. V. S., Varaprasad, R., Mohan, Y. R., Srinivas, T. A. S., & Sravanthi, Y. (2022). Future of smart cities: The role of machine learning and artificial intelligence. South Asian Research Journal of Engineering and Technology, 4(5), 110-119. https://doi.org/10.36346/sarjet.2022.v04i05.005
42. Szpilko, D., Naharro, F. J., Lăzăroiu, G., Nica, E., & de la Torre Gallegos, A. (2023). Artificial intelligence in the smart city—a literature review. Engineering Management in Production and Services, 15(4), 53-75. https://doi.org/ 10.2478/emj-20230028
43. Talebkhah, M., Sali, A., Gordan, M., Hashim, S. J., & Rokhani, F. Z. (2023). Comprehensive review on development of smart cities using industry 4.0 technologies. IEEE Access, 11, 91981-92030.https:// 10.1109/access.2023.3302262
44. Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313–323. https://doi.org/10.1016/j.comcom.2020.02.069
45. Weil, C., Bibri, S. E., Longchamp, R., Golay, F., & Alahi, A. (2023). Urban digital twin challenges: A systematic review and perspectives for sustainable smart cities. Sustainable Cities and Society, 99, 104862. https://doi.org/10.1016/j.scs.2023.104862
46. Wordart, (2025). Source: Created by Wordart, 2025, Avaliable at: https://wordart.com/
47. WoS, (2025). Source: Created by WoS, 2025, Avaliable at: https://www.webofscience.com/
48. Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini-Marques, J., Da Costa, E., & Ioppolo, G. (2019). Can cities become smart without being sustainable? A systematic review of the literature. Sustainable Cities and Society, 45, 348-365. https://doi.org/10.1016/j.scs.2018.11.033
49. Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2020). Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability, 13(16), 8952. https://doi.org/10.3390/su13168952
50. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32.https://doi.org/ 10.1109/JIOT.2014.2306328
51. Zhao L., Wang, J., Liu, J., & Kato, N. (2019). Optimal edge resource allocation in IoT-based smart cities. IEEE Network, 33(2), 30-35. https:// 10.1109/mnet.2019.1800221